Systems and methods for processing images to classify the processed images for digital pathology

ABSTRACT

Systems and methods are disclosed for receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/848,703 filed May 16, 2019, the entire disclosure of which is herebyincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally toimage-based specimen classification and related image processingmethods. More specifically, particular embodiments of the presentdisclosure relate to systems and methods for identifying or verifyingspecimen type or specimen properties based on processing images oftissue specimens.

BACKGROUND

In order to use digital pathology images within a hospital or inresearch environments, it can be important to categorize the specimen'stissue type, the nature of the specimen's acquisition (e.g., prostateneedle biopsy, breast biopsy, breast resection, etc.), and otherrelevant properties of the specimen or the image. In hospital settings,tissue type information may be stored in a laboratory information system(LIS). However, the correct tissue classification information is notalways paired with the image content. For example, a third party may begiven anonymized access to the image content without the correspondingspecimen type label stored in the LIS. Access to LIS content may belimited due to its sensitive content. Additionally, even if an LIS isused to access the specimen type for a digital pathology image, thislabel may be incorrect due to the fact that many components of an LISmay be manually inputted, leaving a large margin for error.

A desire exists for a way to provide solutions for incorrect or missingspecimen type labels for digital pathology images, without necessarilyaccessing an LIS or related information database. The followingdisclosure is directed to systems and methods for addressing this needfor classifying tissue specimens from digital pathology images.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure. The background description provided herein is for thepurpose of generally presenting the context of the disclosure. Unlessotherwise indicated herein, the materials described in this section arenot prior art to the claims in this application and are not admitted tobe prior art, or suggestions of the prior art, by inclusion in thissection.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for identifying or verifying specimen type orspecimen properties from image analysis of tissue specimens.

A method for analyzing an image corresponding to a specimen includes:receiving a target image corresponding to a target specimen, the targetspecimen comprising a tissue sample of a patient; applying a machinelearning model to the target image to determine at least onecharacteristic of the target specimen and/or at least one characteristicof the target image, the machine learning model having been generated byprocessing a plurality of training images to predict at least onecharacteristic, the training images comprising images of human tissueand/or images that are algorithmically generated; and outputting the atleast one characteristic of the target specimen and/or the at least onecharacteristic of the target image.

A system for analyzing an image corresponding to a specimen includes amemory storing instructions; and a processor executing the instructionsto perform a process including receiving a target image corresponding toa target specimen, the target specimen comprising a tissue sample of apatient; applying a machine learning model to the target image todetermine at least one characteristic of the target specimen and/or atleast one characteristic of the target image, the machine learning modelhaving been generated by processing a plurality of training images topredict at least one characteristic, the training images comprisingimages of human tissue and/or images that are algorithmically generated;and outputting the at least one characteristic of the target specimenand/or the at least one characteristic of the target image.

A non-transitory computer-readable medium storing instructions that,when executed by processor, cause the processor to perform a method foranalyzing an image corresponding to a specimen, the method includesreceiving a target image corresponding to a target specimen, the targetspecimen comprising a tissue sample of a patient; applying a machinelearning model to the target image to determine at least onecharacteristic of the target specimen and/or at least one characteristicof the target image, the machine learning model having been generated byprocessing a plurality of training images to predict at least onecharacteristic, the training images comprising images of human tissueand/or images that are algorithmically generated; and outputting the atleast one characteristic of the target specimen and/or the at least onecharacteristic of the target image.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1A illustrates an exemplary block diagram of a system and networkfor determining specimen property or image property informationpertaining to digital pathology image(s), according to an exemplaryembodiment of the present disclosure.

FIG. 1B illustrates an exemplary block diagram of a disease detectionplatform 100, according to an exemplary embodiment of the presentdisclosure.

FIG. 1C illustrates an exemplary block diagram of a specimenclassification platform, according to an exemplary embodiment of thepresent disclosure.

FIGS. 2A and 2B are flowcharts illustrating exemplary methods fordetermining specimen property or image property information pertainingto digital pathology image(s), and using machine learning to classify aspecimen, according to one or more exemplary embodiments of the presentdisclosure.

FIG. 3 is a flowchart of an exemplary embodiment of determining specimenproperty or image property information pertaining to digital pathologyimage(s), according to an exemplary embodiment of the presentdisclosure.

FIG. 4 is a flowchart of an exemplary embodiment of generating and usinga specimen type identification tool, according to an exemplaryembodiment of the present disclosure.

FIG. 5 is a flowchart of an exemplary embodiment of generating and usingan image quality control and/or specimen quality control tool, accordingto an exemplary embodiment of the present disclosure.

FIG. 6 is a flowchart of an exemplary embodiment of generating and usinga prior tissue treatment effect identification tool, according to anexemplary embodiment of the present disclosure.

FIG. 7 depicts an example system that may execute techniques presentedherein.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

The systems, devices, and methods disclosed herein are described indetail by way of examples and with reference to the figures. Theexamples discussed herein are examples only and are provided to assistin the explanation of the apparatuses, devices, systems, and methodsdescribed herein. None of the features or components shown in thedrawings or discussed below should be taken as mandatory for anyspecific implementation of any of these devices, systems, or methodsunless specifically designated as mandatory.

Also, for any methods described, regardless of whether the method isdescribed in conjunction with a flow diagram, it should be understoodthat unless otherwise specified or required by context, any explicit orimplicit ordering of steps performed in the execution of a method doesnot imply that those steps must be performed in the order presented butinstead may be performed in a different order or in parallel.

As used herein, the term “exemplary” is used in the sense of “example,”rather than “ideal.” Moreover, the terms “a” and “an” herein do notdenote a limitation of quantity, but rather denote the presence of oneor more of the referenced items.

Pathology refers to the study of diseases. More specifically, pathologyrefers to performing tests and analysis that are used to diagnosediseases. For example, tissue samples may be placed onto slides to beviewed under a microscope by a pathologist (e.g., a physician that is anexpert at analyzing tissue samples to determine whether anyabnormalities exist). That is, pathology specimens may be cut intomultiple sections, stained, and prepared as slides for a pathologist toexamine and render a diagnosis. When uncertain of a diagnostic findingon a slide, a pathologist may order additional cut levels, stains, orother tests to gather more information from the tissue. Technician(s)may then create new slide(s) which may contain the additionalinformation for the pathologist to use in making a diagnosis. Thisprocess of creating additional slides may be time-consuming, not onlybecause it may involve retrieving the block of tissue, cutting it tomake a new a slide, and then staining the slide, but also because it maybe batched for multiple orders. This may significantly delay the finaldiagnosis that the pathologist renders. In addition, even after thedelay, there may still be no assurance that the new slide(s) will haveinformation sufficient to render a diagnosis.

Pathologists may evaluate cancer and other disease pathology slides inisolation. The present disclosure presents a consolidated workflow forimproving diagnosis of cancer and other diseases. The workflow mayintegrate, for example, slide evaluation, tasks, image analysis andcancer detection artificial intelligence (AI), annotations,consultations, and recommendations in one workstation. In particular,the present disclosure describes various exemplary user interfacesavailable in the workflow, as well as AI tools that may be integratedinto the workflow to expedite and improve a pathologist's work.

For example, computers may be used to analyze an image of a tissuesample to quickly identify whether additional information may be neededabout a particular tissue sample, and/or to highlight to a pathologistan area in which he or she should look more closely. Thus, the processof obtaining additional stained slides and tests may be doneautomatically before being reviewed by a pathologist. When paired withautomatic slide segmenting and staining machines, this may provide afully automated slide preparation pipeline. This automation has, atleast, the benefits of (1) minimizing an amount of time wasted by apathologist determining a slide to be insufficient to make a diagnosis,(2) minimizing the (average total) time from specimen acquisition todiagnosis by avoiding the additional time between when additional testsare ordered and when they are produced, (3) reducing the amount of timeper recut and the amount of material wasted by allowing recuts to bedone while tissue blocks (e.g., pathology specimens) are in a cuttingdesk, (4) reducing the amount of tissue material wasted/discarded duringslide preparation, (5) reducing the cost of slide preparation bypartially or fully automating the procedure, (6) allowing automaticcustomized cutting and staining of slides that would result in morerepresentative/informative slides from samples, (7) allowing highervolumes of slides to be generated per tissue block, contributing to moreinformed/precise diagnoses by reducing the overhead of requestingadditional testing for a pathologist, and/or (8) identifying orverifying correct properties (e.g., pertaining to a specimen type) of adigital pathology image, etc.

The process of using computers to assist pathologists is known ascomputational pathology. Computing methods used for computationalpathology may include, but are not limited to, statistical analysis,autonomous or machine learning, and AI. AI may include, but is notlimited to, deep learning, neural networks, classifications, clustering,and regression algorithms. By using computational pathology, lives maybe saved by helping pathologists improve their diagnostic accuracy,reliability, efficiency, and accessibility. For example, computationalpathology may be used to assist with detecting slides suspicious forcancer, thereby allowing pathologists to check and confirm their initialassessments before rendering a final diagnosis.

Histopathology refers to the study of a specimen that has been placedonto a slide. For example, a digital pathology image may be comprised ofa digitized image of a microscope slide containing the specimen (e.g., asmear). One method a pathologist may use to analyze an image on a slideis to identify nuclei and classify whether a nucleus is normal (e.g.,benign) or abnormal (e.g., malignant). To assist pathologists inidentifying and classifying nuclei, histological stains may be used tomake cells visible. Many dye-based staining systems have been developed,including periodic acid-Schiff reaction, Masson's trichrome, nissl andmethylene blue, and Haemotoxylin and Eosin (H&E). For medical diagnosis,H&E is a widely used dye-based method, with hematoxylin staining cellnuclei blue, eosin staining cytoplasm and extracellular matrix pink, andother tissue regions taking on variations of these colors. In manycases, however, H&E-stained histologic preparations do not providesufficient information for a pathologist to visually identify biomarkersthat can aid diagnosis or guide treatment. In this situation, techniquessuch as immunohistochemistry (IHC), immunofluorescence, in situhybridization (ISH), or fluorescence in situ hybridization (FISH), maybe used. IHC and immunofluorescence involve, for example, usingantibodies that bind to specific antigens in tissues enabling the visualdetection of cells expressing specific proteins of interest, which canreveal biomarkers that are not reliably identifiable to trainedpathologists based on the analysis of H&E stained slides. ISH and FISHmay be employed to assess the number of copies of genes or the abundanceof specific RNA molecules, depending on the type of probes employed(e.g. DNA probes for gene copy number and RNA probes for the assessmentof RNA expression). If these methods also fail to provide sufficientinformation to detect some biomarkers, genetic testing of the tissue maybe used to confirm if a biomarker is present (e.g., overexpression of aspecific protein or gene product in a tumor, amplification of a givengene in a cancer).

A digitized image may be prepared to show a stained microscope slide,which may allow a pathologist to manually view the image on a slide andestimate a number of stained abnormal cells in the image. However, thisprocess may be time consuming and may lead to errors in identifyingabnormalities because some abnormalities are difficult to detect.Computational processes and devices may be used to assist pathologistsin detecting abnormalities that may otherwise be difficult to detect.For example, AI may be used to predict biomarkers (such as theoverexpression of a protein and/or gene product, amplification, ormutations of specific genes) from salient regions within digital imagesof tissues stained using H&E and other dye-based methods. The images ofthe tissues could be whole slide images (WSI), images of tissue coreswithin microarrays or selected areas of interest within a tissuesection. Using staining methods like H&E, these biomarkers may bedifficult for humans to visually detect or quantify without the aid ofadditional testing. Using AI to infer these biomarkers from digitalimages of tissues has the potential to improve patient care, while alsobeing faster and less expensive.

The detected biomarkers or the image alone could then be used torecommend specific cancer drugs or drug combination therapies to be usedto treat a patient, and the AI could identify which drugs or drugcombinations are unlikely to be successful by correlating the detectedbiomarkers with a database of treatment options. This can be used tofacilitate the automatic recommendation of immunotherapy drugs to targeta patient's specific cancer. Further, this could be used for enablingpersonalized cancer treatment for specific subsets of patients and/orrarer cancer types.

In the field of pathology today, it may be difficult to providesystematic quality control (“QC”), with respect to pathology specimenpreparation, and quality assurance (“QA”) with respect to the quality ofdiagnoses, throughout the histopathology workflow. Systematic qualityassurance is difficult because it is resource and time intensive as itmay require duplicative efforts by two pathologists. Some methods forquality assurance include (1) second review of first-time diagnosiscancer cases; (2) periodic reviews of discordant or changed diagnoses bya quality assurance committee; and (3) random review of a subset ofcases. These are non-exhaustive, mostly retrospective, and manual. Withan automated and systematic QC and QA mechanism, quality can be ensuredthroughout the workflow for every case. Laboratory quality control anddigital pathology quality control are critical to the successful intake,process, diagnosis, and archive of patient specimens. Manual and sampledapproaches to QC and QA confer substantial benefits. Systematic QC andQA has the potential to provide efficiencies and improve diagnosticquality.

As described above, computational pathology processes and devices of thepresent disclosure may provide an integrated platform allowing a fullyautomated process including data ingestion, processing and viewing ofdigital pathology images via a web-browser or other user interface,while integrating with a laboratory information system (LIS). Further,clinical information may be aggregated using cloud-based data analysisof patient data. The data may come from hospitals, clinics, fieldresearchers, etc., and may be analyzed by machine learning, computervision, natural language processing, and/or statistical algorithms to doreal-time monitoring and forecasting of health patterns at multiplegeographic specificity levels.

The digital pathology images described above may be stored with tagsand/or labels pertaining to the properties of the specimen or image ofthe digital pathology image, and such tags/labels may be incorrect orincomplete. Accordingly, the present disclosure is directed to systemsand methods for identifying or verifying correct properties (e.g.,pertaining to a specimen type) of a digital pathology image. Inparticular, the disclosed systems and methods may automatically predictthe specimen or image properties of a digital pathology image, withoutrelying on the stored tags/labels. Further, the present disclosure isdirected to systems and methods for quickly and correctly identifyingand/or verifying a specimen type of a digital pathology image, or anyinformation related to a digital pathology image, without necessarilyaccessing an LIS or analogous information database. One embodiment ofthe present disclosure may include a system trained to identify variousproperties of a digital pathology image, based on datasets of priordigital pathology images. The trained system may provide aclassification for a specimen shown in a digital pathology image. Theclassification may help to provide treatment or diagnosis prediction(s)for a patient associated with the specimen.

This disclosure includes one or more embodiments of a specimenclassification tool. The input to the tool may include a digitalpathology image and any relevant additional inputs. Outputs of the toolmay include global and/or local information about the specimen. Aspecimen may include a biopsy or surgical resection specimen.

Exemplary global outputs of the disclosed tool(s) may containinformation about an entire image, e.g., the specimen type, the overallquality of the cut of the specimen, the overall quality of the glasspathology slide itself, and/or tissue morphology characteristics.Exemplary local outputs may indicate information in specific regions ofan image, e.g., a particular image region may be classified as havingblur or a crack in the slide. The present disclosure includesembodiments for both developing and using the disclosed specimenclassification tool(s), as described in further detail below.

FIG. 1A illustrates a block diagram of a system and network fordetermining specimen property or image property information pertainingto digital pathology image(s), using machine learning, according to anexemplary embodiment of the present disclosure.

Specifically, FIG. 1A illustrates an electronic network 120 that may beconnected to servers at hospitals, laboratories, and/or doctors'offices, etc. For example, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125, etc., may each be connected to an electronicnetwork 120, such as the Internet, through one or more computers,servers, and/or handheld mobile devices. According to an exemplaryembodiment of the present application, the electronic network 120 mayalso be connected to server systems 110, which may include processingdevices that are configured to implement a disease detection platform100, which includes a specimen classification tool 101 for determiningspecimen property or image property information pertaining to digitalpathology image(s), and using machine learning to classify a specimen,according to an exemplary embodiment of the present disclosure.

The physician servers 121, hospital servers 122, clinical trial servers123, research lab servers 124, and/or laboratory information systems 125may create or otherwise obtain images of one or more patients' cytologyspecimen(s), histopathology specimen(s), slide(s) of the cytologyspecimen(s), digitized images of the slide(s) of the histopathologyspecimen(s), or any combination thereof. The physician servers 121,hospital servers 122, clinical trial servers 123, research lab servers124, and/or laboratory information systems 125 may also obtain anycombination of patient-specific information, such as age, medicalhistory, cancer treatment history, family history, past biopsy orcytology information, etc. The physician servers 121, hospital servers122, clinical trial servers 123, research lab servers 124, and/orlaboratory information systems 125 may transmit digitized slide imagesand/or patient-specific information to server systems 110 over theelectronic network 120. Server system(s) 110 may include one or morestorage devices 109 for storing images and data received from at leastone of the physician servers 121, hospital servers 122, clinical trialservers 123, research lab servers 124, and/or laboratory informationsystems 125. Server systems 110 may also include processing devices forprocessing images and data stored in the storage devices 109. Serversystems 110 may further include one or more machine learning tool(s) orcapabilities. For example, the processing devices may include a machinelearning tool for a disease detection platform 100, according to oneembodiment. Alternatively or in addition, the present disclosure (orportions of the system and methods of the present disclosure) may beperformed on a local processing device (e.g., a laptop).

The physician servers 121, hospital servers 122, clinical trial servers123, research lab servers 124, and/or laboratory information systems 125refer to systems used by pathologists for reviewing the images of theslides. In hospital settings, tissue type information may be stored in aLIS 125. However, the correct tissue classification information is notalways paired with the image content. Additionally, even if an LIS isused to access the specimen type for a digital pathology image, thislabel may be incorrect due to the fact that many components of an LISmay be manually inputted, leaving a large margin for error. According toan exemplary embodiment of the present disclosure, a specimen type maybe identified without needing to access the LIS 125, or may beidentified to possibly correct LIS 125. For example, a third party maybe given anonymized access to the image content without thecorresponding specimen type label stored in the LIS. Additionally,access to LIS content may be limited due to its sensitive content.

FIG. 1B illustrates an exemplary block diagram of a disease detectionplatform 100 for determining specimen property or image propertyinformation pertaining to digital pathology image(s), using machinelearning.

Specifically, FIG. 1B depicts components of the disease detectionplatform 100, according to one embodiment. For example, the diseasedetection platform 100 may include a specimen classification tool 101, adata ingestion tool 102, a slide intake tool 103, a slide scanner 104, aslide manager 105, a storage 106, and a viewing application tool 108.

The specimen classification tool 101, as described below, refers to aprocess and system for determining specimen property or image propertyinformation pertaining to digital pathology image(s), and using machinelearning to classify a specimen, according to an exemplary embodiment.

The data ingestion tool 102 refers to a process and system forfacilitating a transfer of the digital pathology images to the varioustools, modules, components, and devices that are used for classifyingand processing the digital pathology images, according to an exemplaryembodiment.

The slide intake tool 103 refers to a process and system for scanningpathology images and converting them into a digital form, according toan exemplary embodiment. The slides may be scanned with slide scanner104, and the slide manager 105 may process the images on the slides intodigitized pathology images and store the digitized images in storage106.

The viewing application tool 108 refers to a process and system forproviding a user (e.g., pathologist) with specimen property or imageproperty information pertaining to digital pathology image(s), accordingto an exemplary embodiment. The information may be provided throughvarious output interfaces (e.g., a screen, a monitor, a storage device,and/or a web browser, etc.).

The specimen classification tool 101, and each of its components, maytransmit and/or receive digitized slide images and/or patientinformation to server systems 110, physician servers 121, hospitalservers 122, clinical trial servers 123, research lab servers 124,and/or laboratory information systems 125 over a network 120. Further,server systems 110 may include storage devices for storing images anddata received from at least one of the specimen classification tool 101,the data ingestion tool 102, the slide intake tool 103, the slidescanner 104, the slide manager 105, and viewing application tool 108.Server systems 110 may also include processing devices for processingimages and data stored in the storage devices. Server systems 110 mayfurther include one or more machine learning tool(s) or capabilities,e.g., due to the processing devices. Alternatively or in addition, thepresent disclosure (or portions of the system and methods of the presentdisclosure) may be performed on a local processing device (e.g., alaptop).

Any of the above devices, tools, and modules may be located on a devicethat may be connected to an electronic network 120, such as the Internetor a cloud service provider, through one or more computers, servers,and/or handheld mobile devices.

FIG. 1C illustrates an exemplary block diagram of a specimenclassification tool 101, according to an exemplary embodiment of thepresent disclosure. The specimen classification tool 101 may include atraining image platform 131 and/or a target image platform 135.

According to one embodiment, the training image platform 131 may includea training image intake module 132, a quality score determiner module133, and/or a treatment identification module 134.

The training image platform 131, according to one embodiment, may createor receive training images that are used to train a machine learningmodel, which may also be known as a machine learning system, toeffectively analyze and classify digital pathology images. For example,the training images may be received from any one or any combination ofthe server systems 110, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125. Images used for training may come from realsources (e.g., humans, animals, etc.) or may come from synthetic sources(e.g., graphics rendering engines, 3D models, etc.). Examples of digitalpathology images may include (a) digitized slides stained with a varietyof stains, such as (but not limited to) H&E, Hematoxylin alone, IHC,molecular pathology, etc.; and/or (b) digitized tissue samples from a 3Dimaging device, such as microCT.

The training image intake module 132 may create or receive a datasetcomprising one or more training images corresponding to either or bothof images of a human tissue and images that are graphically rendered.For example, the training images may be received from any one or anycombination of the server systems 110, physician servers 121, hospitalservers 122, clinical trial servers 123, research lab servers 124,and/or laboratory information systems 125. This dataset may be kept on adigital storage device. The quality score determiner module 133 mayidentify quality control (QC) issues (e.g., imperfections) for thetraining images at a global or local level that may greatly affect theusability of a digital pathology image. For example, the quality scoredeterminer module may use information about an entire image, e.g., thespecimen type, the overall quality of the cut of the specimen, theoverall quality of the glass pathology slide itself, or tissuemorphology characteristics, and determine an overall quality score forthe image. The treatment identification module 134 may analyze images oftissues and determine which digital pathology images have treatmenteffects (e.g., post-treatment) and which images do not have treatmenteffects (e.g., pre-treatment). It is useful to identify whether adigital pathology image has treatment effects because prior treatmenteffects in tissue may affect the morphology of the tissue itself. MostLIS do not explicitly keep track of this characteristic, and thusclassifying specimen types with prior treatment effects can be desired.

According to one embodiment, the target image platform 135 may include atarget image intake module 136, a specimen detection module 137, and anoutput interface 138. The target image platform 135 may receive a targetimage and apply the machine learning model to the received target imageto determine a characteristic of a target specimen. For example, thetarget image may be received from any one or any combination of theserver systems 110, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125. The target image intake module 136 may receivea target image corresponding to a target specimen. The specimendetection module 137 may apply the machine learning model to the targetimage to determine a characteristic of the target specimen. For example,the specimen detection module 137 may detect a specimen type of thetarget specimen. The specimen detection module 137 may also apply themachine learning model to the target image to determine a quality scorefor the target image. Further, the specimen detection module 137 mayapply the machine learning model to the target specimen to determinewhether the target specimen is pre-treatment or post-treatment.

The output interface 138 may be used to output information about thetarget image and the target specimen. (e.g., to a screen, monitor,storage device, web browser, etc.).

FIG. 2A is a flowchart illustrating an exemplary method of a tool forclassifying a specimen, according to an exemplary embodiment of thepresent disclosure. For example, an exemplary method 200 (e.g., steps202 to 206) may be performed by the specimen classification tool 101 inresponse to a request from a user (e.g., physician).

According to one embodiment, the exemplary method 200 for classifying aspecimen may include one or more of the following steps. In step 202,the method may include receiving a target image corresponding to atarget specimen, the target specimen comprising a tissue sample of apatient. For example, the target image may be received from any one orany combination of the server systems 110, physician servers 121,hospital servers 122, clinical trial servers 123, research lab servers124, and/or laboratory information systems 125.

In step 204, the method may include applying a machine learning model tothe target image to determine at least one characteristic of the targetspecimen and/or at least one characteristic of the target image.Determining the characteristic of the target specimen may includedetermining a specimen type of the target specimen. Further, accordingto one embodiment, determining the characteristic of the target specimenmay include identifying a confidence value corresponding to the specimentype of the target specimen. For example, the machine learning model mayindicate a level of confidence in the specimen type, according tovarious parameters. This may be done by using a range of means,including, but not limited to, using a neural network to compute aprobability score for one or more characteristics and thresholding thatprobability. An alternative to this approach is to examine the entropyof the outputs produced by a probabilistic machine learning system,where high entropy indicates greater uncertainty. Additionally,determining the characteristic of the target image may includeidentifying a quality score for each of the training images. Forexample, the method may include applying the trained machine learningmodel predicting a presence of quality control (QC) issues. For example,the method may include identifying quality control issues (e.g., poorlycut specimen sections, scanning artifacts, damaged slides, markings onslides, etc.), and/or recommending an action (e.g., rescan of image,recut, slide reconstruction, etc.) to mitigate the issue. According toone embodiment, the determining the characteristic of the target imagemay include identifying an amount of treatment effects in a target imageand outputting a predicted degree to which a tissue of the target imagehas been treated.

The machine learning model may have been generated by processing aplurality of training images to predict at least one characteristic, andthe training images may include images of human tissue and/or imagesthat are algorithmically generated. The machine learning model may beimplemented using machine learning methods for classification andregression. Training inputs could include real or synthetic imagery.Training inputs may or may not be augmented (e.g., adding noise orcreating variants of the input by flipping/distortions). Exemplarymachine learning models may include, but are not limited to, any one orany combination of Neural Networks, Convolutional neural networks,Random Forest, Logistic Regression, and Nearest Neighbor. Convolutionalneural networks can directly learn the image feature representationsnecessary for discriminating among characteristics, which can workextremely well when there are large amounts of data to train on for eachspecimen, whereas the other methods can be used with either traditionalcomputer vision features, e.g., SURF or SIFT, or with learned embeddings(e.g., descriptors) produced by a trained convolutional neural network,which can yield advantages when there are only small amounts of data totrain on. The training images may be received from any one or anycombination of the server systems 110, physician servers 121, hospitalservers 122, clinical trial servers 123, research lab servers 124,and/or laboratory information systems 125. This dataset may be kept on adigital storage device. Images used for training may come from realsources (e.g., humans, animals, etc.) or may come from synthetic sources(e.g., graphics rendering engines, 3D models, etc.). Examples of digitalpathology images may include (a) digitized slides stained with a varietyof stains, such as (but not limited to) H&E, IHC, molecular pathology,etc.; and/or (b) digitized tissue samples from a 3D imaging device, suchas microCT.

In step 206, the method may include outputting the at least onecharacteristic of the target specimen and/or the at least onecharacteristic of the target image. If unable to determine a specimentype, the method may include outputting an alert indicating that thespecimen type of the target specimen is not identifiable.

FIG. 2B is a flowchart illustrating an exemplary method of a tool forclassifying a specimen, according to an exemplary embodiment of thepresent disclosure. For example, an exemplary method 208 (e.g., steps210 to 250) may be performed by the specimen classification tool 101 inresponse to a request from a user (e.g., physician).

According to one embodiment, the exemplary method 208 for classifying aspecimen may include one or more of the following steps. In step 210, amachine learning model may create or receive a dataset comprising one ormore training images corresponding to either or both of images of ahuman tissue and images that are graphically rendered. For example, thetraining images may be received from any one or any combination of theserver systems 110, physician servers 121, hospital servers 122,clinical trial servers 123, research lab servers 124, and/or laboratoryinformation systems 125. This dataset may be kept on a digital storagedevice. Images used for training may come from real sources (e.g.,humans, animals, etc.) or may come from synthetic sources (e.g.,graphics rendering engines, 3D models, etc.). Examples of digitalpathology images may include (a) digitized slides stained with a varietyof stains, such as (but not limited to) H&E, IHC, molecular pathology,etc.; and/or (b) digitized tissue samples from a 3D imaging device, suchas microCT.

In step 220, a machine learning model may be trained to predict acharacteristic of a training specimen of a training image based on oneor more parameters. For example, a machine learning model may have itsparameters fit (e.g., a neural network trained with backpropagation) topredict the labels in the training set, which may allow the model toreplicate the correct output behavior (e.g., corresponding labels) whengiven a digital pathology image as input. This machine learning modelmay be implemented using machine learning methods for classification andregression. Training inputs could include real or synthetic imagery.Training inputs may or may not be augmented (e.g., adding noise).Exemplary machine learning models may include, but are not limited to,any one or any combination of Neural Networks, Convolutional neuralnetworks, Random Forest, Logistic Regression, and Nearest Neighbor.

In step 230, the method may include receiving a digital pathology image(e.g., target image). For example, the target image may be received fromany one or any combination of the server systems 110, physician servers121, hospital servers 122, clinical trial servers 123, research labservers 124, and/or laboratory information systems 125. In step 240, themethod may include applying the machine learning model to the receivedtarget image to determine a characteristic of the target specimen.Determining the characteristic of the target specimen may includedetermining a specimen type of the target specimen. Further, accordingto one embodiment, determining the characteristic of the target specimenmay include identifying a confidence value corresponding to the specimentype of the target specimen. For example, the machine learning model mayindicate a level of confidence in the specimen type, according tovarious parameters. Additionally, determining the characteristic of thetarget image may include identifying a quality score for each of thetraining images. For example, the method may include applying thetrained machine learning model predicting a presence of quality control(QC) issues. The method may include identifying quality control issues(e.g., poorly cut specimen sections, scanning artifacts, damaged slides,markings on slides, etc.), and/or recommending an action (e.g., rescanof image, recut, slide reconstruction, etc.) to mitigate the issue.According to one embodiment, the determining the characteristic of thetarget image may include identifying an amount of treatment effects in atarget image and outputting a predicted degree to which a tissue of thetarget image has been treated.

In step 250, the method may include outputting a characteristic of thetarget specimen to a monitor, digital storage device, etc. If unable todetermine a specimen type, the method may include outputting an alertindicating that the specimen type of the target specimen is notidentifiable.

FIG. 3 illustrates exemplary methods of a tool for determining specimenproperty or image property information pertaining to digital pathologyimage(s), using machine learning. For example, exemplary methods 300 and320 (e.g., steps 301-325) may be performed by the specimenclassification tool 101 in response to a request from a user (e.g.,physician).

According to one embodiment, the exemplary method 300 for developing thespecimen classification tool 101 may include one or more of thefollowing steps. In step 301, a machine learning model may create orreceive a dataset comprising one or more digital pathology images andcorresponding specimen/tissue type labels. For example, the images maybe received from any one or any combination of the server systems 110,physician servers 121, hospital servers 122, clinical trial servers 123,research lab servers 124, and/or laboratory information systems 125.This dataset may be kept on a digital storage device. Images used fortraining may come from real sources (e.g., humans, animals, etc.) or maycome from synthetic sources (e.g., graphics rendering engines, 3Dmodels, etc.). Examples of digital pathology images may include (a)digitized slides stained with a variety of stains, such as (but notlimited to) H&E, IHC, molecular pathology, etc.; and/or (b) digitizedtissue samples from a 3D imaging device, such as microCT.

In step 303, a machine learning model may train a parametric ornon-parametric machine learning model, e.g., in which a machine learningmodel may identify parameters of the images and corresponding labels inthe dataset, which may allow the model to replicate the correct outputbehavior (e.g., corresponding labels) when given a digital pathologyimage as input. This machine learning model may be implemented usingmachine learning methods for classification and regression. Traininginputs could include real or synthetic imagery. Training inputs may ormay not be augmented (e.g., adding noise). Exemplary machine learningmodels may include, but are not limited to any one or any combination ofNeural Networks, Convolutional neural networks, Random Forest, LogisticRegression, and Nearest Neighbor.

In step 305, a machine learning model may be prompted to produce localand global output(s) for the pathology images, e.g., based on the one ormore identified parameters of the machine learning model. Such output(s)may be to a monitor, a digital storage device, etc.

According to one embodiment, an exemplary method 320 for using thespecimen classification tool 101 may include one or more of the stepsbelow. In step 321, the method may include receiving a digital pathologyimage from a user. For example, the image may be received from any oneor any combination of the server systems 110, physician servers 121,hospital servers 122, clinical trial servers 123, research lab servers124, and/or laboratory information systems 125. In step 323, the methodmay include applying the trained system to the digital pathology imageand predict the specimen type. In step 325, the method may includeoutputting a specimen type prediction to a monitor, digital storagedevice, etc.

Further, in step 325, the method may include comparing the predictedspecimen information to the information provided in an LIS or elsewhere.If the predicted information does not match the stored information, oris not within a predetermined margin of the stored information, an alertmay be generated or a system may alter its processing behavior of theinput and/or correct the stored information due to this mismatch. Themethod may include using the predicted specimen type to initiate anothermachine learning model or machine learning model for processing areceived image or related information from a user (e.g., a tissuedonor). Examples may include a diagnostic model to perform an automateddiagnosis from this specific specimen type or providing contextualinformation to a system capable of processing images from many kinds oftissues. If the specimen type cannot be identified, the method mayinclude generating an alert to the system or user.

The above-described specimen classification tool 101 may includeparticular applications or embodiments usable in research, and/orproduction/clinical/industrial settings. These are described in detailbelow.

An exemplary method of identifying specimen types may be used for manyapplications of digital pathology. For example, identifying specimentypes may be desired for institutions that receive access to digitalpathology images, where the image information or access to the imageinformation lacks corresponding specimen type information (e.g., from anLIS). Identification may also be desired for internal hospital usage ifdigital pathology images need to be sent to specimen-specific diagnosisor diagnosis-aide tools. Identification may be used as a form ofverification to ensure that an LIS-provided specimen type label isindeed correct.

FIG. 4 illustrates an exemplary method for a specimen typeidentification tool. For example, an exemplary method 400 may beperformed by the specimen classification tool 101 in response to arequest from a user (e.g., physician). According to one embodiment, theexemplary method 400 for developing a specimen type identification toolmay include one or more of the steps below. In step 401, a machinelearning model may create or receive a dataset of digital pathologyimages and their corresponding specimen type label. For example, theimages may be received from any one or any combination of the serversystems 110, physician servers 121, hospital servers 122, clinical trialservers 123, research lab servers 124, and/or laboratory informationsystems 125. This dataset may include one or more specimen types thatthe model is intended to classify. This step may also includedetermining the one or more specimen types that the model is intended toclassify. This dataset may be kept on a digital storage device. Datasetswith one specimen type may be used to verify LIS-given specimen typelabels. Datasets with many specimen types may be used for broaderidentification purposes.

In step 403, a machine learning model may train a machine learning modelto classify each digital pathology image according to its specimen type.This model may take the digital pathology images and correspondingspecimen type labels as inputs. This model may be implemented using anymachine learning classification model. Examples of implementations mayinclude, but are not limited to any one or any combination of NeuralNetworks, Random Forest, Logistic Regression, Nearest Neighbor, andDensity estimation approaches. Convolutional neural networks maydirectly learn the image feature representations used for discriminatingthe specimen type, which may work well if there are large amounts ofdata to train on for each specimen, whereas the other methods may beused with either traditional computer vision features, e.g., SURF orSIFT, or with learned embeddings (e.g., descriptors) produced by atrained convolutional neural network, which may yield advantages ifthere are smaller amounts of data to train on. In step 405, a machinelearning model may be prompted to output labels for an individualpathology image to a digital storage device.

According to one embodiment, an exemplary method 420 for using thespecimen type identification tool may include one or more of the stepsbelow. In step 421, the method may include receiving a digital pathologyimage. For example, the image may be received from any one or anycombination of the server systems 110, physician servers 121, hospitalservers 122, clinical trial servers 123, research lab servers 124,and/or laboratory information systems 125. In step 423, the method mayinclude applying the trained machine learning model of the exemplaryspecification type tool to the received digital pathology image andpredict the specimen type. In step 425, the method may includeoutputting a specimen type prediction for an image label, e.g., to ascreen, monitor, storage device, etc. If the machine learning model isbeing used to verify an existing specimen type label for a digitalpathology image, the machine learning model may output an indication ofconfidence or fit for the given label to a screen, monitor, storagedevice, etc.

FIG. 5 illustrates an exemplary embodiment of a specimen classificationtool 101 that may be used to identify quality control (QC) issues (e.g.,imperfections) at a global or local level that may greatly affect theusability of a digital pathology image. For example, exemplary methods500 and 520 (e.g., steps 501-525) may be performed by the specimenclassification tool 101 in response to a request from a user (e.g.,physician). The exemplary method 500 may be useful given that QC relatedinformation for digital pathology images may not be stored in an LIS orany digital storage device.

According to one embodiment, the exemplary method 500 for developing aquality control tool may include one or more of the steps below. In step501, a machine learning model may create or receive a real or syntheticdataset of digital pathology images that may include examples of QCissues and give each image a global or local label(s) to indicatepresence of a QC issue. For example, the images may be received from anyone or any combination of the server systems 110, physician servers 121,hospital servers 122, clinical trial servers 123, research lab servers124, and/or laboratory information systems 125. This dataset may be kepton a digital storage device. QC labels may include but are not limitedto: poorly cut specimen sections, scanning artifacts, damaged slides,markings on slides, etc.

In step 503, a machine learning model may train a machine learning modelto classify each digital pathology slide in relation to its quality.This model may take the digitized pathology images and corresponding QClabels as input. Exemplary machine learning models may include, but arenot limited to any one or any combination of Neural Networks, RandomForest, Logistic Regression, and Nearest Neighbor. In step 505, amachine learning model may be prompted to output a label indicating thepresence of a QC issue for an individual pathology image to a digitalstorage device.

According to one embodiment, an exemplary method 520 for using a QC toolmay include one or more of the steps below. In step 521, the method mayinclude obtaining or receiving a digital pathology image. For example,the image may be received from any one or any combination of the serversystems 110, physician servers 121, hospital servers 122, clinical trialservers 123, research lab servers 124, and/or laboratory informationsystems 125. In step 523, the method may include applying the trainedmachine learning model of the QC tool and predict presence of QC issues.In step 525, the method may include outputting a prediction to a digitalstorage device, e.g., to a screen, monitor, storage device, etc. Themethod may include outputting the type of QC issue (poorly cut specimen,scanning artifacts, etc.), and/or recommending an action (rescan ofimage, recut, etc.) to mitigate the issue, e.g., to a screen, monitor,storage device, etc. The method may include outputting whether the QCissue on the image directly affects the tissue itself. This may beuseful for understanding if the digital image is still useable by apathologist, e.g., to a screen, monitor, storage device, etc.

FIG. 6 illustrates an exemplary embodiment of generating and using aprior tissue treatment effect identification tool, according to anexemplary embodiment of the present disclosure. Prior treatment effectsin tissue may affect the morphology of the tissue itself. Most LIS donot explicitly keep track of this characteristic, and thus classifyingspecimen types with prior treatment effects can be desired. A system fordetecting treatment effects in one or more tissue types is describedbelow. For example, exemplary methods 600 and 620 (e.g., steps 601-625)may be performed by the specimen classification tool 101 in response toa request from a user (e.g., physician).

According to one embodiment, the exemplary method 600 for developing aprior tissue treatment effect identification tool may include one ormore of the steps below. In step 601, a machine learning model maycreate or receive a dataset of digital pathology images that includeimages of tissues that have treatment effects and images of tissues thatdo not have treatment effects. For example, the images may be receivedfrom any one or any combination of the server systems 110, physicianservers 121, hospital servers 122, clinical trial servers 123, researchlab servers 124, and/or laboratory information systems 125. This datasetmay either contain images for a single tissue type or multiple tissuetypes. This dataset may be kept on a digital storage device.

In step 603, a machine learning model may train a machine learning modelto classify each digital pathology image as being treated (e.g.,post-treatment) or untreated (pre-treatment). If the patient hastreatment effects, the model may also be trained on the degree oftreatment effects. This model may take the digitized pathology imagesand corresponding treatment effect labels as input. This model may betrained using supervised learning classification methods or unsuperviseddensity estimation or anomaly detection methods. Examples of supervisedlearning implementations may include any one or any combination ofNeural Networks, Random Forest, Logistic Regression, and NearestNeighbor. In step 605, a machine learning model may be prompted tooutput a label indicating a presence of treatment effects for anindividual pathology image to a digital storage device (e.g.,post-treatment).

According to one embodiment, an exemplary method 620 for using the priortissue treatment effect identification tool may include one or more ofthe steps below. In step 621, the method may include obtaining orreceiving a digital pathology image. For example, the image may bereceived from any one or any combination of the server systems 110,physician servers 121, hospital servers 122, clinical trial servers 123,research lab servers 124, and/or laboratory information systems 125. Instep 623, the method may include applying the trained machine learningmodel of the exemplary prior treatment effect tool and predict apresence of treatment effects. In step 625, the method may includeoutputting the prediction, e.g., to a screen, monitor, storage device,etc. The method may include outputting an indication of a degree towhich the tissue of the pathology image has been treated, e.g., to ascreen, monitor, storage device, etc.

As shown in FIG. 7, device 700 may include a central processing unit(CPU) 720. CPU 720 may be any type of processor device including, forexample, any type of special purpose or a general-purpose microprocessordevice. As will be appreciated by persons skilled in the relevant art,CPU 720 also may be a single processor in a multi-core/multiprocessorsystem, such system operating alone, or in a cluster of computingdevices operating in a cluster or server farm. CPU 720 may be connectedto a data communication infrastructure 710, for example, a bus, messagequeue, network, or multi-core message-passing scheme.

Device 700 also may include a main memory 740, for example, randomaccess memory (RAM), and also may include a secondary memory 730.Secondary memory 730, e.g., a read-only memory (ROM), may be, forexample, a hard disk drive or a removable storage drive. Such aremovable storage drive may comprise, for example, a floppy disk drive,a magnetic tape drive, an optical disk drive, a flash memory, or thelike. The removable storage drive in this example reads from and/orwrites to a removable storage unit in a well-known manner. The removablestorage unit may comprise a floppy disk, magnetic tape, optical disk,etc., which is read by and written to by the removable storage drive. Aswill be appreciated by persons skilled in the relevant art, such aremovable storage unit generally includes a computer usable storagemedium having stored therein computer software and/or data.

In alternative implementations, secondary memory 730 may include othersimilar means for allowing computer programs or other instructions to beloaded into device 700. Examples of such means may include a programcartridge and cartridge interface (such as that found in video gamedevices), a removable memory chip (such as an EPROM, or PROM) andassociated socket, and other removable storage units and interfaces,which allow software and data to be transferred from a removable storageunit to device 700.

Device 700 also may include a communications interface (“COM”) 760.Communications interface 760 allows software and data to be transferredbetween device 700 and external devices. Communications interface 760may include a modem, a network interface (such as an Ethernet card), acommunications port, a PCMCIA slot and card, or the like. Software anddata transferred via communications interface 760 may be in the form ofsignals, which may be electronic, electromagnetic, optical, or othersignals capable of being received by communications interface 760. Thesesignals may be provided to communications interface 760 via acommunications path of device 700, which may be implemented using, forexample, wire or cable, fiber optics, a phone line, a cellular phonelink, an RF link or other communications channels.

The hardware elements, operating systems and programming languages ofsuch equipment are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. Device 700 alsomay include input and output ports 750 to connect with input and outputdevices such as keyboards, mice, touchscreens, monitors, displays, etc.Of course, the various server functions may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load. Alternatively, the servers may be implemented byappropriate programming of one computer hardware platform.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules can be implemented in software,hardware, or a combination of software and hardware.

The tools, modules, and functions described above may be performed byone or more processors. “Storage” type media may include any or all ofthe tangible memory of the computers, processors or the like, orassociated modules thereof, such as various semiconductor memories, tapedrives, disk drives and the like, which may provide non-transitorystorage at any time for software programming.

Software may be communicated through the Internet, a cloud serviceprovider, or other telecommunication networks. For example,communications may enable loading software from one computer orprocessor into another. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

The foregoing general description is exemplary and explanatory only, andnot restrictive of the disclosure. Other embodiments of the inventionwill be apparent to those skilled in the art from consideration of thespecification and practice of the invention disclosed herein. It isintended that the specification and examples be considered as exemplaryonly.

What is claimed is:
 1. A computer-implemented method for analyzing animage corresponding to a specimen, the method comprising: receiving atarget image corresponding to a target specimen, the target specimencomprising a tissue sample of a patient; applying a machine learningsystem to the target image to determine at least one characteristic ofthe target specimen and/or at least one characteristic of the targetimage, the machine learning system having been generated by processing aplurality of training images to predict at least one characteristic, thetraining images comprising images of human tissue and/or images that arealgorithmically generated; outputting the at least one characteristic ofthe target specimen and/or the at least one characteristic of the targetimage; identifying a quality score for the target image, the qualityscore being determined according to the machine learning system;determining whether the quality score for the target image is less thana predetermined value; and in response to the quality score for thetarget image being less than the predetermined value, outputting arecommendation for increasing a quality of the target image.
 2. Thecomputer-implemented method of claim 1, further comprising: determininga prediction of a specimen type of the target specimen based on the atleast one characteristic of the target specimen; and outputting theprediction of the specimen type of the target specimen.
 3. Thecomputer-implemented method of claim 1, further comprising: determininga prediction of a specimen type of the target specimen based on the atleast one characteristic of the target specimen; and in response todetermining that a confidence value of the prediction does not exceed apredetermined threshold, outputting an alert indicating that thespecimen type of the target specimen is not identifiable.
 4. Thecomputer-implemented method of claim 1, further comprising: determininga confidence value of a prediction of a specimen type of the targetspecimen based on the at least one characteristic of the targetspecimen; and outputting the confidence value.
 5. Thecomputer-implemented method of claim 1, further comprising: outputtingthe quality score.
 6. The computer-implemented method of claim 1,wherein the recommendation comprises any one or any combination of aspecimen cut, a scanning parameter, a slide reconstruction, and a slidemarking.
 7. The computer-implemented method of claim 1, furthercomprising: determining, using the target image and the machine learningsystem, whether the target specimen is post-treatment or pre-treatment;upon determining that the target specimen is post-treatment, determininga predicted degree to which the target specimen has been treated basedon the target image; and outputting the predicted degree to which thetarget specimen has been treated.
 8. A system for analyzing an imagecorresponding to a specimen, the system comprising: at least one memorystoring instructions; and at least one processor executing theinstructions to perform operations comprising: receiving a target imagecorresponding to a target specimen, the target specimen comprising atissue sample of a patient; applying a machine learning system to thetarget image to determine at least one characteristic of the targetspecimen and/or at least one characteristic of the target image, themachine learning system having been generated by processing a pluralityof training images to predict at least one characteristic, the trainingimages comprising images of human tissue and/or images that arealgorithmically generated; outputting the at least one characteristic ofthe target specimen and/or the at least one characteristic of the targetimage; identifying a quality score for the target image, the qualityscore being determined according to the machine learning system;determining whether the quality score for the target image is less thana predetermined value; and in response to the quality score for thetarget image being less than the predetermined value, outputting arecommendation for increasing a quality of the target image.
 9. Thesystem of claim 8, the operations further comprising: determining aprediction of a specimen type of the target specimen based on the atleast one characteristic of the target specimen; and outputting theprediction of the specimen type of the target specimen.
 10. The systemof claim 8, the operations further comprising: determining a predictionof a specimen type of the target specimen based on the at least onecharacteristic of the target specimen; and in response to determiningthat a confidence value of the prediction does not exceed apredetermined threshold, outputting an alert indicating that thespecimen type of the target specimen is not identifiable.
 11. The systemof claim 8, the operations further comprising: determining a confidencevalue of a prediction of a specimen type of the target specimen based onthe at least one characteristic of the target specimen; and outputtingthe confidence value.
 12. The system of claim 8, the operations furthercomprising: outputting the quality score.
 13. The system of claim 8,wherein the recommendation comprises any one or any combination of aspecimen cut, a scanning parameter, a slide reconstruction, and a slidemarking.
 14. The system of claim 8, the operations further comprising:determining, using the target image and the machine learning system,whether the target specimen is post-treatment or pre-treatment; upondetermining that the target specimen is post-treatment, determining apredicted degree to which the target specimen has been treated based onthe target image; and outputting the predicted degree to which thetarget specimen has been treated.
 15. A computer-implemented method foranalyzing an image corresponding to a specimen, the method comprising:receiving a target image corresponding to a target specimen, the targetspecimen comprising a tissue sample of a patient; applying a machinelearning system to the target image to determine at least onecharacteristic of the target specimen and/or at least one characteristicof the target image, the machine learning system having been generatedby processing a plurality of training images to predict at least onecharacteristic, the training images comprising images of human tissueand/or images that are algorithmically generated; outputting the atleast one characteristic of the target specimen and/or the at least onecharacteristic of the target image; and determining, using the targetimage and the machine learning system, whether the target specimen ispost-treatment or pre-treatment.
 16. The computer-implemented method ofclaim 15, further comprising: determining a prediction of a specimentype of the target specimen based on the at least one characteristic ofthe target specimen; and outputting the prediction of the specimen typeof the target specimen.
 17. The computer-implemented method of claim 15,further comprising: determining a prediction of a specimen type of thetarget specimen based on the at least one characteristic of the targetspecimen; and in response to determining that a confidence value of theprediction does not exceed a predetermined threshold, outputting analert indicating that the specimen type of the target specimen is notidentifiable.
 18. The computer-implemented method of claim 15, furthercomprising: determining a confidence value of a prediction of a specimentype of the target specimen based on the at least one characteristic ofthe target specimen; and outputting the confidence value.
 19. Thecomputer-implemented method of claim 15, further comprising: upondetermining that the target specimen is post-treatment, determining apredicted degree to which the target specimen has been treated based onthe target image; and outputting the predicted degree to which thetarget specimen has been treated.
 20. A system for analyzing an imagecorresponding to a specimen, the system comprising: at least one memorystoring instructions; and at least one processor executing theinstructions to perform operations comprising: receiving a target imagecorresponding to a target specimen, the target specimen comprising atissue sample of a patient; applying a machine learning system to thetarget image to determine at least one characteristic of the targetspecimen and/or at least one characteristic of the target image, themachine learning system having been generated by processing a pluralityof training images to predict at least one characteristic, the trainingimages comprising images of human tissue and/or images that arealgorithmically generated; outputting the at least one characteristic ofthe target specimen and/or the at least one characteristic of the targetimage; and determining, using the target image and the machine learningsystem, whether the target specimen is post-treatment or pre-treatment.